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CRM record maintenance checklist for every month and quarter

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min read
Repeated record-cleanup checklists arranged on a timeline

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TL;DR

  • A monthly CRM check looks at what entered the CRM since the last check.
  • A quarterly CRM check asks whether your duplicate searches still match your team's definition.
  • Merge the obvious duplicates first, like exact matches on email or domain.
  • Reject a match only when you're certain the records aren't duplicates.
  • For monthly and quarterly checks, Dedupely finds duplicates, previews merges and sends weekly reports.

A first cleanup gets a CRM back in shape. Keeping it there takes a routine, because forms, imports and integrations keep adding records, and some of them are duplicates.

Routines tend to slip when nobody knows what to check. A short list for each month and each quarter makes the check repeatable, and easy to hand to someone else.

This post is that list. You'll get a monthly checklist for what's new, a quarterly checklist for what's changed, and a summary table you can copy. How often your team should check depends on your sources, which our post on how often to deduplicate your CRM covers.

Before you start: agree on what a duplicate is

A checklist only works if everyone checks for the same thing. What counts as a duplicate is case by case: what one team considers a duplicate, another may not.

Write your definition down per object, along with the field that identifies each record, like email for contacts and domain for companies. Give the checklist one owner. They don't merge everything alone, but they make sure the check happens and bring unclear cases to the right team.

If you work in Salesforce, our Salesforce data governance guide covers the wider policy around standards, ownership and roles. This checklist is the routine that runs inside it.

Monthly CRM record maintenance checklist

The monthly check is about what's new, and it's the shorter of the two.

1. Look at new duplicates by source

Find the duplicates created since your last check, and note where they came from: a form, an import, an integration or manual entry. A cluster on one date often points to one import.

Write down the count. Month over month, it shows whether a source is getting better or worse.

2. Merge the obvious duplicates

Start with exact matches on the field that identifies each record, like email or domain. These are the duplicates your team is least likely to debate.

If you merge some duplicates automatically, open a sample of last month's merges. Check that each one is a merge you'd have made by hand.

3. Check this month's imports

Event lists, purchased lists and spreadsheets from other teams arrive in bursts. Look for duplicates from each import while the source is still fresh in everyone's mind. Our post on how event and purchased lists create duplicates covers why they're a frequent source.

4. Work through the matches in review

Unclear matches wait in review until someone decides. Merge the ones you've confirmed. Reject a match only when you're certain it isn't a duplicate, since a rejected match won't come back in your searches.

Leave the rest for the people who know the records, such as the account owner.

5. Look for records that can't be matched

Check new records missing the field that identifies them, like a contact with no email. Also look for placeholder values such as test@test.com or "John Doe". Both make future duplicates harder to find, and placeholders can group unrelated records together.

6. Note anything new that writes to the CRM

Ask whether any form, integration or tool started writing to the CRM this month. Add it to a running list with the date. When a new duplicate pattern appears later, that list tells you where it came from.

Quarterly CRM record maintenance checklist

The quarterly check is about what's changed: your sources, your rules and your team. It takes longer, and it's worth involving the teams that use the records.

1. Look for duplicate patterns no search covers

Your searches find the duplicates you already know about. Each quarter, try one new combination, such as name with company or name with country. You can also allow small typos on one field.

Change one thing at a time. If the count jumps, open a sample and check whether the new matches are real duplicates.

2. Check that your definition still holds

Teams change how they use records. Sales may start tracking branches separately, or marketing may switch to account-based campaigns. Ask each team whether what they consider a duplicate has changed.

If it has, update your searches and any automatic merging before the next month's check.

3. Review which values win in a merge

Look at merged records from the quarter. Check the fields that often cause trouble when they're wrong: owner, lifecycle stage and email opt-out.

If a merged record kept a value your team didn't want, adjust the rule for that field.

4. Review every tool that writes to the CRM

Go through your running list of forms, integrations and imports, checking field mappings. Confirm each source matches on the field your team agreed on, since a changed mapping can cause a spike of duplicates. Our post on recurring duplicates from CRM integrations goes deeper.

5. Check records owned by people who left

When a record's owner leaves, someone who can't find the record may create it again. Reassign those records, and check for duplicates created around each departure.

6. Compare each quarter with the one before

Put the monthly counts side by side. Look at new duplicates per source, merges made and matches still in review. Bring the numbers to the teams that use the records, so they see what changed.

7. Plan any large merge

If the quarterly check finds a large new pattern, plan its merge on its own. Decide whether you need an export first. Our guide on backing up your CRM before a large merge helps you choose.

CRM record maintenance checklist at a glance

When Task What you're looking for
Monthly New duplicates by source Which source added duplicates since the last check
Monthly Merge the obvious duplicates Exact matches on email or domain; a sample of automatic merges
Monthly This month's imports Duplicates from event lists, purchased lists and spreadsheets
Monthly Matches in review Merge what's confirmed; reject only what's certain
Monthly Records that can't be matched Missing emails or domains; placeholder values
Monthly New tools writing to the CRM Anything added to the running list
Quarterly New duplicate patterns Combinations no search covers yet
Quarterly Your definition of a duplicate Changes in how teams use records
Quarterly Which values win Owner, lifecycle stage and opt-out after merges
Quarterly Tools that write to the CRM Field mappings and the field each source matches on
Quarterly Records of people who left Unowned records and duplicates created around a departure
Quarterly Quarter over quarter Trends in new duplicates, merges and review
Quarterly Large merges Whether a new pattern needs its own plan and an export

How Dedupely supports your monthly and quarterly checks

In Dedupely, you build duplicate searches for each pattern on your checklist, send unclear sets to review and preview each merge before it runs. By default, you get a duplicate report every week, and every merge is recorded with its original values. When you're certain a match isn't a duplicate, you reject it once, and it stays out of your searches.

Set up your CRM hygiene routine

A routine your team can repeat

CRM record maintenance works best as a routine with an owner. The monthly check handles what's new: duplicates by source, recent imports, the review list and records that can't be matched.

The quarterly check handles what's changed: new patterns, your team's definition of a duplicate, merge decisions and the tools that write to the CRM. Run both, compare the numbers over time, and each check leaves a little less to find.

Frequently asked questions

What should a CRM record maintenance checklist include?

A monthly part for what's new and a quarterly part for what's changed. Monthly, check new duplicates by source, recent imports, matches in review and records missing key fields. Quarterly, look for new duplicate patterns, confirm your definition of a duplicate, review merge decisions and check every tool that writes to the CRM.

What should a monthly CRM data check include?

A monthly check covers what entered the CRM since the last one. Look at new duplicates by source, check recent imports, work through matches in review and look for records missing email or domain. Also note any form, integration or tool that started writing to the CRM, so later patterns are easy to trace.

What is CRM record maintenance?

CRM record maintenance is the routine work that keeps CRM records accurate and usable. It includes finding and merging duplicates, filling or fixing key fields like email and domain, and removing placeholder data. It also covers checking the forms, imports and integrations that add records, so problems are caught early.

Who should own CRM record maintenance?

One named person, often in RevOps or the team that administers the CRM. The owner makes sure each check happens on schedule and notes what it found. Unclear matches go to the people who know the records, and one or two backups cover when the owner is away.

What should a quarterly CRM audit cover?

Look for duplicate patterns no search covers yet, and confirm each team's definition of a duplicate still holds. Review which values won in recent merges, especially owner and lifecycle stage. Check field mappings on every connected tool, and compare the quarter's duplicate counts with the one before.

Portrait of Gaby Salinas

Behind the article

Gaby Salinas

Marketing Assistant at Dedupely

Gaby Salinas came to CRM data from marketing and advertising, so she looks at duplicates from two sides: the match and merge logic behind them, and what they do to the people using the CRM, like the rep who calls a customer twice, the admin who gets blamed and the cleanup nobody wants to start.

As Marketing Assistant at Dedupely, she follows how RevOps teams, end users, consultants and technical teams actually work with their data: the records they stop trusting, the workarounds they build and the moment they finally decide to clean up. What she finds shapes most of what Dedupely publishes.

Connect with Gaby on LinkedIn

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